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Predicting Biomarkers and Therapeutic Targets in Cancer

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Computational Intelligence in Oncology

Part of the book series: Studies in Computational Intelligence ((SCI,volume 1016))

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Abstract

The identification of specific patient therapeutic strategies, drug reactions, and efficacy of the management during the early period of cancer may help in guiding the medical oncologists in the selection of anti-cancer treatment for a person suffering from cancer. Also, it helps in calculating the improvable of the toxicity to benefit ratio. The success of this treatment is due to the emergence of predictive biomarkers. A predictive biomarker is a simple tool usually measured before treatment and provides information on the probability of response to a particular therapy. Several beneficial reports and appropriate evidence-based on the roles of prognostic and predictive biomarkers of known therapeutic targets in different cancer types including breast, colorectal, and non-small cell lung cancers in adults are also observed from different reviews. However, it is found that the utilization of predictive biomarkers incorporating with some drugs derived from natural sources such as trabectedin, cabazitaxel, and alvocidib is a bit slower than usual. Thus, this review paper covers the recent advances of cancer biomarkers which are used to forecast the effectiveness of selected natural compounds focusing on human clinical studies.

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Authors and Affiliations

  1. Department of Vegetable Science, College of Horticulture and Forestry, Central Agricultural University, Pasighat, Arunachal Pradesh, 791102, India

    Rina Ningthoujam, Naorem BidyaLeima Chanu & Vivek Anumala

  2. Biomaterials and Bioengineering Research Laboratory, Department of Biotechnology, Pachhunga University College, Mizoram University (A Central University), Aizawl, Mizoram, 796001, India

    Punuri Jayasekhar Babu

  3. Department of Botany, School of Applied Sciences, Centurion University of Technology and Management, Bhubaneswar, Odisha, India

    Srimay Pradhan

  4. CSIR—Institute of Minerals and Materials Technology, Bhubaneswar, 751013, India

    Manasa Kumar Panda

  5. Department of Natural Resource Management, College of Horticulture and Forestry, Central Agricultural University, Pasighat, Arunachal Pradesh, 791102, India

    Punabati Heisnam

  6. Biomaterials and Bioprocessing Research Laboratory, Department of Post-Harvest Technology, College of Horticulture and Forestry, Central Agricultural University, Pasighat, Arunachal Pradesh, 791102, India

    Yengkhom Disco Singh

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  1. Department of Computer Science, Jamia Millia Islamia, New Delhi, India

    Khalid Raza

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Ningthoujam, R. et al. (2022). Predicting Biomarkers and Therapeutic Targets in Cancer. In: Raza, K. (eds) Computational Intelligence in Oncology. Studies in Computational Intelligence, vol 1016. Springer, Singapore. https://doi.org/10.1007/978-981-16-9221-5_13

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